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arXiv · 1901.03760

Residual Pyramid FCN for Robust Follicle Segmentation

Zhewei Wang·Weizhen Cai·Charles D. Smith·Noriko Kantake·Thomas J. Rosol·Jundong Liu
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Abstract

In this paper, we propose a pyramid network structure to improve the FCN-based segmentation solutions and apply it to label thyroid follicles in histology images. Our design is based on the notion that a hierarchical updating scheme, if properly implemented, can help FCNs capture the major objects, as well as structure details in an image. To this end, we devise a residual module to be mounted on consecutive network layers, through which pixel labels would be propagated from the coarsest layer towards the finest layer in a bottom-up fashion. We add five residual units along the decoding path of a modified U-Net to make our segmentation network, Res-Seg-Net. Experiments demonstrate that the multi-resolution set-up in our model is effective in producing segmentations with improved accuracy and robustness.

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Zhewei Wang, Weizhen Cai, Charles D. Smith, Noriko Kantake, Thomas J. Rosol, Jundong Liu. 2019-01-11. Residual Pyramid FCN for Robust Follicle Segmentation. https://arxiv.org/abs/1901.03760

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